A Framework for Quantifying and Reducing Uncertainty in InfoSymbiotic Systems Arising in Atmospheric Environments

Abstract

This project focuses on large scale dynamic data driven applications systems (DDDAS, or InfoSymbiotic systems) governed by partial differential equations (PDEs), e.g., arising in atmospheric environments. Specifically, our main interests are data assimilation and the configuration of sensor networks. During this project we have developed a rigorous framework for quantifying and reducing uncertainty in the context of InfoSymbiotic systems. This includes a goal-oriented aposteriori error estimation methodology for the impact of different errors on the variational solutions of inverse problems; an optimization-constrained optimization problem approach to find the optimal configuration of the DDDAS system; new parallel-in-time algorithms to speed up variational inference; a trust-region approach to perform inference in an ensemble space; new nonlinear filtering and smoothing algorithms that sample directly from the posterior PDF using a Hybrid Markov-Chain Monte Carlo(HMCMC) approach; and a solid theoretical basis for optimization with reduced order models.

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Document Details

Document Type
Technical Report
Publication Date
Feb 29, 2016
Accession Number
AD1004746

Entities

People

  • Adrian Sandu

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Assimilation
  • Atmospheric Motion
  • Computational Science
  • Control Systems
  • Detectors
  • Differential Equations
  • Electronic Mail
  • Equations
  • Filtration
  • Inverse Problems
  • Markov Chains
  • Mathematics
  • Monte Carlo Method
  • Networks
  • Operations Research
  • Optimization
  • Partial Differential Equations
  • Sampling
  • Scientific Research
  • Sensor Networks
  • Shallow Water
  • Statistical Algorithms
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space